Reckoning Machines

A process and technology advisory firm.

Forecasts that keep their reasoning.

Reckoning Machines builds production systems for investment firms, with AI models inside the research process, and Monte Carlo decision models for corporate finance teams.

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Advisory

The work starts inside the client's own forecast.

We build the system and run it alongside the team until it is handed over.

Investment firms

We have twice built a systematic equity platform and taken it through to live trading. The most recent one carries portfolio construction, optimization, order management and execution, with an LLM runner making about 11,000 calls per cycle inside the data pipeline. For a fundamental research team the same engineering puts AI models inside the research process with IRIS.

Corporate finance and FP&A

For a global payments company we built the profitability and unit-economics model for a new product and paired it with Monte Carlo scenario analysis. A large pharmaceutical company needed a way to decide which AI-enabled commercial investments to fund and which to test first. We built that decision framework on Monte Carlo and value-of-information analysis.

Point of view

Cheap models move the value to the process around them.

A frontier model can read a 10-K and draft a forecast assumption at almost no cost, so the cost of building a forecast model has collapsed. Analysis that used to be an edge is becoming a commodity, and the advantage moves to the process around the forecast. Teams that keep their forecasts in one place, with assumptions written down and testable, get more from the same AI models than teams that paste answers into a spreadsheet.

AI that proposes, a record of what it proposed, and a person who decides.

Built by Reckoning Machines

Two systems built on the same rule.

ExcelClaude alongside

IRIS

IRIS makes Claude for Excel smarter about your financial models.

  • Claude gives the analyst a natural interface to build and work with financial models.
  • IRIS Model Logic understands the workbook's calculations and dependencies.
  • IRIS economic understanding provides a structured interpretation of what those calculations represent.
  • Methods turn forecasting assumptions into explicit, evidence-backed, revisable investment judgment.
  • Memory gives the analyst's judgment continuity through time.
  • Approved, versioned execution makes those decisions operational and reproducible.

Semantic compression ties it together by giving Claude a compact account of the model, so it spends tokens on the question rather than on rediscovering the spreadsheet every time.

Analysts start from the IRIS Model Catalog rather than a blank sheet.

  • Day oneIRIS makes Claude better informed about the model.
  • Over timeIRIS remembers how the analyst thinks about the company.
  • EventuallyThat persistent understanding supports proactive model maintenance and investment reasoning across the book.

Supervised pilot with two investment firms. Access is arranged with us.

irisos.dev
DialogPaper book

Adaptive Research Harness | DIALOG

The Adaptive Research Harness holds research agents to account for what they try, and Dialog is the trading desk that runs on it.

  • Research agents now produce more ideas than a team can check, and the best-looking result of many attempts is not the same as one that will hold up.
  • ARH writes each attempt to the record before the next can start, with its question, its reserved budget, its result and its failures, so the count of attempts is a fact of the record rather than a recollection.
  • A separate evaluator checks each proposed conclusion, and a result enters the accepted record only under rules the owner has set.
  • Rejected work stays in the record, so the next researcher sees the failed variants and the decisions behind what was accepted.
  • Researchers and evaluators can be replaced while the acceptance rules stay in place, so a team can change its agents without losing what it has learned.
  • Dialog puts the harness to work on a paper desk. Competing research approaches develop hypotheses, an independent evaluator checks them, and the strategies that pass share one portfolio with orders, fills, cash and fees accounted for.

Budget makes the search visible and separate acceptance makes the result accountable, so when a strategy is challenged the history shows how much searching it took to find it and why it was accepted.

Open prototypes on GitHub. Synthetic data and paper execution.

View ARH on GitHub View DIALOG on GitHub

About

Run by an investor who builds the systems.

Jed Gore

Jed Gore, CFA, started as a software engineer on prime brokerage systems. He then spent nineteen years investing in financial-services stocks, five of them as portfolio manager of a $250 million portfolio at Diamondback Capital. Since 2017 he has built research and analytics platforms for investors. He has been the sole engineer on two systematic equity platforms taken to live trading, and ran the portfolio on one of them. He is a named inventor on a U.S. patent for extracting tables from financial filings.

Contact

One email starts it.

Write to us about a forecast you run or a system you want built.

Email Reckoning Machines